Because communicating is so critical to functioning in the world, disorders of speech production are among the most debilitating neurological conditions. Developing effective treatments will require accurate, interpretable models of the neural processes controlling speaking. In defining such models, the role of sensory feedback has been a key issue: speaking appears to be both a feedforward process (you can speak with sensory feedback blocked) and a feedback process (alteration of sensory feedback modifies speech). One way to model this duality is to take an existing model of speech motor control based on sensory feedback control and augment it with a separate feedforward controller. This is the approach taken in DIVA, a currently dominant model of speech motor control, where feedback and feedforward control subsystems combine their outputs in motor cortex to control the vocal tract. In our lab, however, we have been investigating another way of modeling the feedforward and feedback characteristics of speech called observer-based, state feedback control (SFC). Here, control of speech is based entirely on feedback control, but the feedback comes from a surrogate called an observer that is only indirectly affected by real sensory feedback. Both models can account for the behavioral characteristics of speaking, but they make very different and testable predictions about the underlying neural processes responsible for those behaviors. Here, we will test the differing predictions of these two models by perturbing the auditory feedback of subjects as they speak and examining their neural responses to these feedback perturbations using several different functional neuroimaging methods: magnetoencephalographic imaging (MEG-I) and electrocorticography (ECoG). Outside of speech motor research, SFC models of other motor behaviors (e.g. reaching, eye movements) are becoming more prevalent, in large part because people appear to move in optimal ways (i.e., minimizing expended energy, only controlling task-relevant aspects of their movements) and SFC is the foundation of modern optimal control theory. If the neural control of speaking were shown to be consistent with an SFC model, we could relate it to other domains of motor control research and leverage an extensive theoretical knowledge base, allowing us to make powerful predictions of the model's behavior.